# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import unittest
from functools import partial
from typing import Any

import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import SkipReasons, TrtLayerAutoScanTest

import paddle.inference as paddle_infer


class TrtConvertConcatTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        inputs = program_config.inputs
        weights = program_config.weights
        outputs = program_config.outputs

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]
        # The input dimension should be less than or equal to the set axis.
        if len(inputs['concat_input1'].shape) <= attrs[0]['axis']:
            return False

        return True

    def sample_program_configs(self):
        def generate_input1(attrs: list[dict[str, Any]], batch):
            if self.dims == 4:
                return np.ones([batch, 3, 24, 24]).astype(np.float32)
            elif self.dims == 3:
                return np.ones([batch, 3, 24]).astype(np.float32)
            elif self.dims == 2:
                return np.ones([batch, 24]).astype(np.float32)
            elif self.dims == 1:
                return np.ones([24]).astype(np.float32)

        def generate_input2(attrs: list[dict[str, Any]], batch):
            if self.dims == 4:
                return np.ones([batch, 3, 24, 24]).astype(np.float32)
            elif self.dims == 3:
                return np.ones([batch, 3, 24]).astype(np.float32)
            elif self.dims == 2:
                return np.ones([batch, 24]).astype(np.float32)
            elif self.dims == 1:
                return np.ones([24]).astype(np.float32)

        def generate_input3(attrs: list[dict[str, Any]], batch):
            if self.dims == 4:
                return np.ones([batch, 3, 24, 24]).astype(np.float32)
            elif self.dims == 3:
                return np.ones([batch, 3, 24]).astype(np.float32)
            elif self.dims == 2:
                return np.ones([batch, 24]).astype(np.float32)
            elif self.dims == 1:
                return np.ones([24]).astype(np.float32)

        def generate_weight1(attrs: list[dict[str, Any]]):
            return np.zeros([1]).astype(np.int32)

        for dims in [2, 3, 4]:
            for num_input in [0, 1]:
                for batch in [1, 2, 4]:
                    for axis in [-1, 0, 1, 2, 3]:
                        self.num_input = num_input
                        self.dims = dims
                        dics = [{"axis": axis}, {}]
                        dics_input = [
                            {
                                "X": [
                                    "concat_input1",
                                    "concat_input2",
                                    "concat_input3",
                                ],
                                "AxisTensor": ["AxisTensor"],
                            },
                            {
                                "X": [
                                    "concat_input1",
                                    "concat_input2",
                                    "concat_input3",
                                ]
                            },
                        ]
                        dics_inputs = [
                            {
                                "AxisTensor": TensorConfig(
                                    data_gen=partial(generate_weight1, dics)
                                ),
                                "concat_input3": TensorConfig(
                                    data_gen=partial(
                                        generate_input3, dics, batch
                                    )
                                ),
                                "concat_input2": TensorConfig(
                                    data_gen=partial(
                                        generate_input2, dics, batch
                                    )
                                ),
                                "concat_input1": TensorConfig(
                                    data_gen=partial(
                                        generate_input1, dics, batch
                                    )
                                ),
                            },
                            {
                                "concat_input3": TensorConfig(
                                    data_gen=partial(
                                        generate_input3, dics, batch
                                    )
                                ),
                                "concat_input2": TensorConfig(
                                    data_gen=partial(
                                        generate_input2, dics, batch
                                    )
                                ),
                                "concat_input1": TensorConfig(
                                    data_gen=partial(
                                        generate_input1, dics, batch
                                    )
                                ),
                            },
                        ]
                        ops_config = [
                            {
                                "op_type": "concat",
                                "op_inputs": dics_input[num_input],
                                "op_outputs": {"Out": ["concat_output"]},
                                "op_attrs": dics[0],
                            }
                        ]
                        ops = self.generate_op_config(ops_config)
                        program_config = ProgramConfig(
                            ops=ops,
                            weights={},
                            inputs=dics_inputs[num_input],
                            outputs=["concat_output"],
                        )

                        yield program_config

    def generate_dynamic_shape(self, attrs):
        if self.num_input == 0:
            if self.dims == 4:
                self.dynamic_shape.min_input_shape = {
                    "concat_input1": [1, 3, 24, 24],
                    "concat_input2": [1, 3, 24, 24],
                    "concat_input3": [1, 3, 24, 24],
                    "AxisTensor": [1],
                }
                self.dynamic_shape.max_input_shape = {
                    "concat_input1": [4, 3, 48, 48],
                    "concat_input2": [4, 3, 48, 48],
                    "concat_input3": [4, 3, 48, 48],
                    "AxisTensor": [1],
                }
                self.dynamic_shape.opt_input_shape = {
                    "concat_input1": [1, 3, 24, 24],
                    "concat_input2": [1, 3, 24, 24],
                    "concat_input3": [1, 3, 24, 24],
                    "AxisTensor": [1],
                }
            elif self.dims == 3:
                self.dynamic_shape.min_input_shape = {
                    "concat_input1": [1, 3, 24],
                    "concat_input2": [1, 3, 24],
                    "concat_input3": [1, 3, 24],
                    "AxisTensor": [1],
                }
                self.dynamic_shape.max_input_shape = {
                    "concat_input1": [4, 12, 48],
                    "concat_input2": [4, 12, 48],
                    "concat_input3": [4, 12, 48],
                    "AxisTensor": [1],
                }
                self.dynamic_shape.opt_input_shape = {
                    "concat_input1": [1, 3, 24],
                    "concat_input2": [1, 3, 24],
                    "concat_input3": [1, 3, 24],
                    "AxisTensor": [1],
                }
            elif self.dims == 2:
                self.dynamic_shape.min_input_shape = {
                    "concat_input1": [1, 24],
                    "concat_input2": [1, 24],
                    "concat_input3": [1, 24],
                    "AxisTensor": [1],
                }
                self.dynamic_shape.max_input_shape = {
                    "concat_input1": [4, 48],
                    "concat_input2": [4, 48],
                    "concat_input3": [4, 48],
                    "AxisTensor": [1],
                }
                self.dynamic_shape.opt_input_shape = {
                    "concat_input1": [1, 24],
                    "concat_input2": [1, 24],
                    "concat_input3": [1, 24],
                    "AxisTensor": [1],
                }
            elif self.dims == 1:
                self.dynamic_shape.min_input_shape = {
                    "concat_input1": [24],
                    "concat_input2": [24],
                    "concat_input3": [24],
                    "AxisTensor": [0],
                }
                self.dynamic_shape.max_input_shape = {
                    "concat_input1": [48],
                    "concat_input2": [48],
                    "concat_input3": [48],
                    "AxisTensor": [0],
                }
                self.dynamic_shape.opt_input_shape = {
                    "concat_input1": [24],
                    "concat_input2": [24],
                    "concat_input3": [24],
                    "AxisTensor": [0],
                }
        elif self.num_input == 1:
            if self.dims == 4:
                self.dynamic_shape.min_input_shape = {
                    "concat_input1": [1, 3, 24, 24],
                    "concat_input2": [1, 3, 24, 24],
                    "concat_input3": [1, 3, 24, 24],
                }
                self.dynamic_shape.max_input_shape = {
                    "concat_input1": [4, 3, 48, 48],
                    "concat_input2": [4, 3, 48, 48],
                    "concat_input3": [4, 3, 48, 48],
                }
                self.dynamic_shape.opt_input_shape = {
                    "concat_input1": [1, 3, 24, 24],
                    "concat_input2": [1, 3, 24, 24],
                    "concat_input3": [1, 3, 24, 24],
                }
            elif self.dims == 3:
                self.dynamic_shape.min_input_shape = {
                    "concat_input1": [1, 3, 24],
                    "concat_input2": [1, 3, 24],
                    "concat_input3": [1, 3, 24],
                }
                self.dynamic_shape.max_input_shape = {
                    "concat_input1": [4, 12, 48],
                    "concat_input2": [4, 12, 48],
                    "concat_input3": [4, 12, 48],
                }
                self.dynamic_shape.opt_input_shape = {
                    "concat_input1": [1, 3, 24],
                    "concat_input2": [1, 3, 24],
                    "concat_input3": [1, 3, 24],
                }
            elif self.dims == 2:
                self.dynamic_shape.min_input_shape = {
                    "concat_input1": [1, 24],
                    "concat_input2": [1, 24],
                    "concat_input3": [1, 24],
                }
                self.dynamic_shape.max_input_shape = {
                    "concat_input1": [4, 48],
                    "concat_input2": [4, 48],
                    "concat_input3": [4, 48],
                }
                self.dynamic_shape.opt_input_shape = {
                    "concat_input1": [1, 24],
                    "concat_input2": [1, 24],
                    "concat_input3": [1, 24],
                }
            elif self.dims == 1:
                self.dynamic_shape.min_input_shape = {
                    "concat_input1": [24],
                    "concat_input2": [24],
                    "concat_input3": [24],
                }
                self.dynamic_shape.max_input_shape = {
                    "concat_input1": [48],
                    "concat_input2": [48],
                    "concat_input3": [48],
                }
                self.dynamic_shape.opt_input_shape = {
                    "concat_input1": [24],
                    "concat_input2": [24],
                    "concat_input3": [24],
                }
        return self.dynamic_shape

    def sample_predictor_configs(
        self, program_config, run_pir=False
    ) -> tuple[paddle_infer.Config, list[int], float]:
        def clear_dynamic_shape():
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        def generate_trt_nodes_num(attrs, dynamic_shape):
            if dynamic_shape:
                return 1, 4
            else:
                if attrs[0]['axis'] != 0:
                    return 1, 4
                else:
                    return 0, 5

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]
        if not run_pir:
            # for static_shape
            clear_dynamic_shape(attrs)
            self.trt_param.precision = paddle_infer.PrecisionType.Float32
            program_config.set_input_type(np.float32)
            yield (
                self.create_inference_config(),
                generate_trt_nodes_num(attrs, False),
                1e-5,
            )
            self.trt_param.precision = paddle_infer.PrecisionType.Half
            program_config.set_input_type(np.float16)
            yield (
                self.create_inference_config(),
                generate_trt_nodes_num(attrs, False),
                1e-3,
            )

        # for dynamic_shape
        self.generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            1e-5,
        )
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        program_config.set_input_type(np.float16)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            1e-3,
        )

    def add_skip_trt_case(self):
        def teller1(program_config, predictor_config):
            if len(program_config.inputs) == 4:
                return True
            return False

        self.add_skip_case(
            teller1, SkipReasons.TRT_NOT_SUPPORT, "INPUT AxisTensor NOT SUPPORT"
        )

    def test(self):
        self.add_skip_trt_case()
        self.run_test(run_pir=True)


if __name__ == "__main__":
    unittest.main()
